The world is awash with discussion tied to the age of Big Data and the need for all organizations (large or small) to take advantage of the business improvement opportunities that harvesting Big Data can bring to light. Yet, to the casual observer, the push for Big Data looks more like a race to see what organization in which industry can amass the most, store the most, and sift through the most data—all in hopes of finding elusive golden nuggets of business insight. The communications industry, in particular, has been generating massive quantities of data for years. Operational and monetization data comes from several sources including: network signaling for establishing a voice call or data session; data networks as customers push and pull data to and from the Internet; the billing processes; and data from outside sources in a variety of formats. The golden nuggets gained from analyzing this data can yield customer usage insight, patterns of need tied to improved operations, detection of fraud situations, opportunities to add business value that will hopefully translate to increased profitability, and insight on how to meet new network investment requirements.
There are multiple communications service provider (CSP) processes and business disciplines in place today, some reaching back years or more. These, and several more recently defined disciplines, are well attuned to the importance of the insights that data analysis can provide. Some of these disciplines include: customer notification and threshold analysis; cloud services enablement; customer service assurance and experience analysis; revenue assurance; business cost analysis; fraud management; network usage optimization and planning; cybersecurity; and virtualized networking. The key objective from each rests within an organization's ability to harvest the right levels of insight, at the right time, in order to address the key business problems that impact business outcomes. Put another way, the goal is to learn from the past to improve business performance for the future.
But what if the future is very different from the past? Remember how touch screen technology changed the user device market in mid-2007 with the introduction of the iPhone. Then, three years later the market twisted again as the tablet was introduced. Did we see this substantial change coming then? What about the mobile app market that now provides millions of apps to consumers and business customers? We saw it coming, but not with the level of success it has experienced to date. What about incorporation of mobile technology into the goods and services of nearly every industry in order to provide a better experience for their customers? This broad incorporation of mobile technology continues to unfold quietly, with little opportunity to look back on what is successful, or how success is even defined. Looking back will not give the insight to plan forward as these and other disruptive events continue to shape and change the communications marketplace.
This week's SPIE focuses on what many believe is the top of the data analysis pyramid—decision intelligence. The report explains what decision intelligence is about, why decision intelligence is important, and why it is essential for making critical business decisions for the future. The report points out that traditional data analysis has its place. However, the results from traditional methods are not keeping up with the rate of change in most industries, including the communications sector. The report also explains how one data solution supplier—Quantellia—is simplifying the complex decision analysis process.
What Big Data Collection and Analysis Provides Today
Big Data continues to be a focal point for most industries. Suppliers are many; and the amount of money spent by CSPs and enterprises in all sectors now surpasses several billion dollars annually. The importance of this data for addressing customer and business challenges cannot be overemphasized. In fact, Stratecast has a dedicated practice tied to the evolution of the Big Data market, the addressable needs of organizations pertaining to the use of Big Data, and the business opportunities that Big Data affords to both the supplier and enterprise marketplace.
The traditional approach to data involves collecting the data, analyzing the data, and determining the types of business problems the data analysis reveals. The need for extracting the maximum value from data to help solve strategic business problems is escalating, but there is no roadmap from the data cloud to the decision process that will lead to desired outcomes. A myriad of systems, people, and analysis does not equate to a systematic approach. The myth that anything done in the data cloud is worth doing, because it makes one agnostic and able to make good decisions, might have been true in the past, but the landscape has shifted. Today, the data cloud is enormous, but most business decisions do not need this overwhelming amount of data. As will be explained in a later section of this report, too much data can create a different set of problems.
Table Of Contents
Decision Intelligence Boosts Analytical Insight for New Network Designs and Business Monetization Strategies Introduction What Big Data Collection and Analysis Provides Today Answering the Tough Questions that Shape Tomorrow, Today The Decision Intelligence Pyramid: Introducing a Complex Systems Model to Learn From the Future, Not Just the Past Visual Modeling of the Decision Intelligence Business Discipline Stratecast - The Last Word About Stratecast About Frost and Sullivan